Register Now

Data quality monitoring, unified data pipelines and ML with Lightup, Delta Lake and Amazon SageMaker 

In this virtual workshop, we’ll cover the best practices for enterprises to use with powerful open source technologies to simplify and scale your data and ML efforts. We’ll discuss how to leverage Apache Spark™ — the de facto data processing and analytics engine for data preparation that unifies data at a massive scale across various sources — and Delta Lake so you can make your data lake ML-ready. You’ll also learn how to use data and ML frameworks, such as TensorFlow, XGBoost and scikit-learn, to train models based on different requirements. 

You’ll also hear from our partner Lightup,  Lightup empowers data-driven companies to quickly implement continuous data quality checks and monitoring with ease. Lightup prevents bad data inputs into models which lead to bad ML model outcomes. With deep integration with the Databricks Lakehouse Platform, Lightup provides an additional layer of data reliability— empowering enterprises to perform in-place data quality checks without the need for ETL. Lightup ensures the quality of datasets for reporting, ML, and customer facing applications.  A major focus area is optimizing migrations from legacy big data systems to Databricks, all with a single click. 

And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment and manage the deployment of models to production on Amazon SageMaker.
 
 Event Agenda

• 9:00–9:15 AM        Welcome and Keynote From Databricks + Lightup
• 9:15–9:30 AM         Customer Presentation
• 9:30–10:50 AM      Technical Hands-On Workshop
• 10:50–11:00 AM      Lightup Presentation and Demo
• 11:00–11:05 AM       Prizes and Next Steps